EoN.estimate_SIR_prob_size

EoN.estimate_SIR_prob_size(G, p, *, rng=None)

Uses percolation to estimate the probability and size of epidemics assuming constant transmission probability p

From figure 6.12 of Kiss, Miller, & Simon. Please cite the book if using this algorithm.

Provies an estimate of epidemic probability and size assuming a fixed transmission probability p.

The estimate is found by performing bond percolation and then finding the largest connected component in the remaining network.

This assumes that there is a single giant component above threshold.

It will not be an appropriate measure if the network is made up of several densely connected components with very weak connections between these components.

Arguments:

G networkx Graph

The network the disease will transmit through.

p number

transmission probability

rng random number generator

If None, will be set to np.random.default_rng()

Returns:

PE, AR both floats between 0 and 1

estimates of the probability and proportion infected (attack rate) in epidemics (the two are equal, but each given for consistency with estimate_directed_SIR_prob_size)

SAMPLE USE:

import networkx as nx
import EoN

G = nx.fast_gnp_random_graph(1000,0.002)
PE, AR = EoN.estimate_SIR_prob_size(G, 0.6)